Search Results for author: Daniel Whiteson

Found 18 papers, 5 papers with code

The Landscape of Unfolding with Machine Learning

no code implementations29 Apr 2024 Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.

Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion

no code implementations22 Apr 2024 Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions.

Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks

no code implementations5 Sep 2023 Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson.

Single Particle Analysis

Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

no code implementations19 May 2023 Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive.

Geometry-aware Autoregressive Models for Calorimeter Shower Simulations

no code implementations16 Dec 2022 Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson

Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors.


Machine-Learning Compression for Particle Physics Discoveries

no code implementations20 Oct 2022 Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson

In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

no code implementations15 Sep 2022 Phiala Shanahan, Kazuhiro Terao, Daniel Whiteson

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community.

SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention

1 code implementation7 Jun 2021 Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics.

Feasibility of Correlated Extensive Air Shower Detection with a Distributed Cosmic Ray Network

no code implementations6 Feb 2021 Eric Albin, Daniel Whiteson

We explore the sensitivity offered by a global network of cosmic ray detectors to a novel, unobserved phenomena: widely separated simultaneous extended air showers.

High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics

Learning to Identify Electrons

1 code implementation3 Nov 2020 Julian Collado, Jessica N. Howard, Taylor Faucett, Tony Tong, Pierre Baldi, Daniel Whiteson

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information.

Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

1 code implementation19 Oct 2020 Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques.

Single Particle Analysis

Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes

no code implementations17 Sep 2017 Meghan Frate, Kyle Cranmer, Saarik Kalia, Alexander Vandenberg-Rodes, Daniel Whiteson

We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals.

Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

no code implementations10 Mar 2017 Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard

We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.

Jet Tagging

Parameterized Machine Learning for High-Energy Physics

2 code implementations28 Jan 2016 Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson

We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.

BIG-bench Machine Learning Vocal Bursts Intensity Prediction

Searching for Higgs Boson Decay Modes with Deep Learning

no code implementations NeurIPS 2014 Peter J. Sadowski, Daniel Whiteson, Pierre Baldi

Particle colliders enable us to probe the fundamental nature of matter by observing exotic particles produced by high-energy collisions.

BIG-bench Machine Learning

Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning

no code implementations13 Oct 2014 Pierre Baldi, Peter Sadowski, Daniel Whiteson

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5$\sigma$ significance barrier without more data.

Bayesian Optimization

Searching for Exotic Particles in High-Energy Physics with Deep Learning

2 code implementations19 Feb 2014 Pierre Baldi, Peter Sadowski, Daniel Whiteson

Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features.

High Energy Physics - Phenomenology High Energy Physics - Experiment

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